Sensory-Aware Sequential Recommendation via Review-Distilled Representations

TL;DR

This paper introduces ASER, a framework that extracts sensory attributes from reviews to enhance item representations in sequential recommendation systems, leading to improved accuracy and interpretability.

Contribution

The paper presents a novel attribute extraction and distillation pipeline that integrates sensory semantics into recommendation models, improving performance across multiple domains.

Findings

Sensory-enhanced models outperform non-sensory counterparts in 19 out of 20 cases.

Average relative gains of 7.9% in HR@10 and 11.2% in NDCG@10.

Extracted attributes align well with human perceptions, enabling interpretability.

Abstract

We propose a novel framework for sensory-aware sequential recommendation that enriches item representations with linguistically extracted sensory attributes from product reviews. Our approach, ASER (Attribute-based Sensory-Enhanced Representation), introduces an offline extraction-and-distillation pipeline in which a large language model is first fine-tuned as a teacher to extract structured sensory attribute-value pairs, such as color: matte black and scent: vanilla, from unstructured review text. The extracted structures are then distilled into a compact student transformer that produces fixed-dimensional sensory embeddings for each item. These embeddings encode experiential semantics in a reusable form and are incorporated into standard sequential recommender architectures as additional item-level representations. We evaluate our method on five Amazon domains and integrate the learned sensory embeddings into SASRec, BERT4Rec, BSARec, and DIFF. Across 20 domain-backbone combinations, sensory-enhanced models improve over matched non-sensory counterparts in 19 cases for both HR@10 and NDCG@10, with average relative gains of 7.9% in HR@10 and 11.2% in NDCG@10. Qualitative analysis further shows that the extracted attributes align closely with human perceptions of products, enabling interpretable connections between natural language descriptions and recommendation behavior. Overall, this work demonstrates that sensory attribute distillation offers a principled and scalable way to bridge information extraction and sequential recommendation through structured semantic representation learning.